基于ILO的时滞随机分布系统故障诊断与容错控制

Fault diagnosis and fault-tolerant control for time-delay stochastic distribution system based on Iterative Learning Observer (ILO)

文中针对时滞随机分布系统,基于迭代学习观测器(ILO)设计了故障诊断和容错控制的算法。首先,采用线性B样条去逼近输出概率密度函数(PDF),使原来的随机分布系统转换为权向量动态系统。其次,针对故障系统,利用前一时刻的故障估计值和残差设计ILO,并且基于Lyapunov稳定性理论对ILO误差系统的稳定性进行分析,从而保障故障的准确估计。再次,构建包含系统状态和PDF权值的增广系统,根据故障估计信息,通过比例积分(PI)控制使得输出PDF仍可以跟踪给定的分布,从而实现容错控制的目标。最后,通过Simulink仿真验证了文中方法的有效性。

In this paper,a fault diagnosis and fault-tolerant control algorithm is designed based on Iterative Learning Observer(ILO)for time-delay stochastic distribution system.Firstly,a linear B-spline is used to approximate the output probability density function(PDF),so that the original random distribution system is transformed into a weighted vector dynamic system.Secondly,for the fault system,the ILO is designed using the fault estimation and residual at the previous moment,and the stability of the ILO error system is analyzed based on the Lyapunov stability theorem so as to ensure the accurate estimation of faults.Thirdly,an augmented system containing system status and PDF weights is constructed,and the output PDF can still track the given distribution through proportional integration(PI)control,so as to achieve the goal of fault-tolerant control.Finally,Simulink simulation verifies the effectiveness of the proposed method.